Follicular Immune Landscaping Reveals a Distinct Profile of FOXP3hiCD4hi T Cells in Treated Compared to Untreated HIV

Follicular helper CD4hi T cells (TFH) are a major cellular pool for the maintenance of the HIV reservoir. Therefore, the delineation of the follicular (F)/germinal center (GC) immune landscape will significantly advance our understanding of HIV pathogenesis. We have applied multiplex confocal imaging, in combination with the relevant computational tools, to investigate F/GC in situ immune dynamics in viremic (vir-HIV), antiretroviral-treated (cART HIV) People Living With HIV (PLWH) and compare them to reactive, non-infected controls. Lymph nodes (LNs) from viremic and cART PLWH could be further grouped based on their TFH cell densities in high-TFH and low-TFH subgroups. These subgroups were also characterized by different in situ distributions of PD1hi TFH cells. Furthermore, a significant accumulation of follicular FOXP3hiCD4hi T cells, which were characterized by a low scattering in situ distribution profile and strongly correlated with the cell density of CD8hi T cells, was found in the cART-HIV low-TFH group. An inverse correlation between plasma viral load and LN GrzBhiCD8hi T and CD16hiCD15lo cells was found. Our data reveal the complex GC immune landscaping in HIV infection and suggest that follicular FOXP3hiCD4hi T cells could be negative regulators of TFH cell prevalence in cART-HIV.


Introduction
Despite the intense research in HIV pathogenesis and cure approaches over the last 40 years, several aspects related to relevant immune cell and viral dynamics are still not well understood.Combination antiretroviral therapy (cART) has extended the life expectancy and improved the quality of life of People Living With HIV (PLWH).Despite the blocking of HIV replication, cART cannot eradicate the virus [1,2].The latent infection of resting CD4 hi T cells is a main contributor to the lifelong persistence of HIV [3].Integrated HIV DNA can be detected in blood and peripheral tissues, but recent studies suggest that genetically intact proviruses are mainly detected in the lymph nodes (LNs) [4].Chronic HIV infection results in dramatic changes in LN architecture and the loss of stromal cells and adaptive immune cell responses [5].Follicular helper CD4 hi T cells (T FH ), a pivotal mediator of efficient B cell responses to pathogens [6,7], represent a major contributor to the HIV reservoir within the central memory CD4 hi T cell compartment [8,9].Non-human primate (NHP) studies have shown an accumulation of T FH cells in chronically SIV-infected compared to noninfected animals, a profile associated with an increased frequency of activated germinal center B cells and secretion of SIV-specific antibodies [10].Progression to AIDS (acquired immunodeficiency syndrome, advanced stage of disease) was associated with an advanced loss of T FH cells in SIV-infected NHPs [11].In addition to T FH and GC B cell altered dynamics, chronic HIV/SIV results in the increased cell density of LN/follicular effector CD8 hi /GrzB hi CD8 hi T cells [12], deregulated immune-regulatory (T REG ) and follicular immune-regulatory (T FR ) CD4+ cells [13,14], the infiltration of inflammatory cell subsets and excessive fibrosis [5,15].Foxp3 hi CD4 hi T cells were reported to suppress the capacity of T FH cells for proliferation and cytokine secretion, in ex vivo HIV-focused studies [14].Additionally, the IL-10 and CTLA-4 expression of TFR cells were up-regulated in treatmentnaïve PLWH [16].Innate immune cells, which contribute to adaptive immune responses either by antigen presentation or by the secretion of immunomodulatory cytokines [17], play an important role in HIV pathogenesis [18,19], while their role in cure strategies [20] is a field under development.
Several studies have focused on the characterization of LN-derived T cells, mainly using LN-derived cell suspensions and flow or mass cytometry-based assays [21] or singlecell RNA analysis [22,23].However, these experimental approaches lack information about immune cell spatial organization.The in situ characterization of immune cell types, using multiplex imaging methodologies and appropriate cohorts of control and disease samples, can provide important insights into the prevalence, phenotype, and spatial organization of the relevant immune cells in HIV, which could also indicate possible mechanistic interactions between specific cell types.
Herein, we applied multiplex imaging combined with advanced computational tools for the comprehensive characterization of immune landscaping in viremic and cART HIV LNs compared to reactive control LNs or tonsils from non-HIV infected individuals.Our results indicate that both viremic and cART HIV LNs can be further grouped based on T FH cell density.These groups exhibited distinct profiles of Foxp3 hi CD4 hi T, Granzyme B (GrzB) hi CD8 hi T and innate immune cell subsets.Our work points to a possible role of FOXP3 hi CD4 hi T cells as regulators of T FH cells in HIV, particularly in cART individuals.Our neighboring analysis revealed a distinct distribution pattern for both T FH and follicular (F-)FOXP3 hi CD4 hi T between PLWH subgroups, as well as compared to noninfected donors.

Human Material
The tissue samples used in this study were obtained from (i) the Centro de Investigacion en Enfermedades Infecciosas (CIENI), Instituto Nacional de Enfermedades Respiratorias (INER) in Mexico City, Mexico (viremic LNs), (ii) the University of Washington, Seattle, WA, USA (cART HIV LNs) and (iii) the archives of the Institute of Pathology of Lausanne University Hospital, Switzerland (control LNs).Tonsillar tissues were obtained from anonymized children who underwent routine tonsillectomy at the Hospital de l'Enfance of Lausanne.All procedures were in accordance with the Declaration of Helsinki and approved by the appropriate Institutional Review Board/Ethical Committee: (i) all tissue samples from PWH were procured with explicit written informed consent from participants prior to donation, adhering strictly to the principles outlined in the Declaration of Helsinki.

Tissue Processing
Fresh tissues were fixed as soon as possible after biopsy for 16-24 h in formalin or 4% paraformaldehyde and processed for the preparation of formalin-fixed, paraffin-embedded (FFPE) blocks using standard procedures at the corresponding pathology departments.All downstream tissue processing was carried out in our laboratory.The blocks were sequentially cut into 4 µm sections and prepared on Superfrost glass slides (Thermo Scientific, Waltham, MA, USA, Ref. J1800AMNZ), dried overnight at 37  C for 20 min.This melting step ensures the proper adherence and deparaffinization of the tissue section.Fluorescent multiplex immunohistochemistry (mIHC) staining was performed on the Ventana Discovery Ultra Autostainer from Roche Diagnostics (Ventana Medical Systems, Tucson, AZ, USA).

Tissue Staining and Data Acquisition
Tissue sections were sequentially subjected to antibody blocking using Opal blocking/antibody diluent solution (ARD1001EA) staining with primary antibodies (details on antibodies, clones and dilutions are listed in Supplementary Table S1), incubation with secondary HRP-labeled antibodies for 16 min, detection with optimized fluorescent Opal tyramide signal amplification (TSA) dyes (Opal 7-color Auto-mation IHC kit, from Akoya (Marlborough, MA, USA), Ref. NEL821001KT) and repeated antibody denaturation cycles.The samples were then counterstained with Spectral DAPI from Akoya for 4 min, rinsed in soapy water and mounted using DAKO mounting medium (Dako/Agilent, Santa Clara, CA, USA, Ref. S302380-2).
Images were acquired using a Leica Stellaris 8 SP8 confocal system, equipped with Leica Application Suite X (LAS-X)-4.6.1.27508software, at 512 × 512 pixel density and 0.75× optical zoom using a 20× objective (NA), unless otherwise stated.Frame averaging or summing was never used while obtaining the images.At least 70% of each section was imaged, to ensure an accurate representation and minimize selection bias.Tissues stained with a single antibody-fluorophore combination were used to create a compensation matrix via the Leica LAS-AF Channel Dye Separation module (Leica Microsystems, Wetzlar, Germany), which was used to correct fluorophore spillover (when present), as per the user's manual.

Quantitative Imaging Analysis (Histo-Cytometry)
A confocal image analysis was performed with Imaris software version 9.9.0 (Bitplane).Quantitative data were generated from the images through Histo-cytometry analysis [12,24], as previously reported.In brief, the Surface Creation module of Imaris was used to generate 3-dimensional segmented surfaces (based on the nuclear signal) of spillover-corrected images.Data generated from Histo-cytometry, such as average voxel intensities for all channels, in addition to the volume and sphericity of the 3-dimensional surfaces, were exported in Microsoft Excel format.The files were converted to comma separated value (.CVS) files, and the data were imported into FlowJo (version 10) to be further analyzed and quantitated.Well-defined areas devoid of background staining were included in the analysis, and the data were quantified either as relative frequencies or as cell counts normalized to the total follicular area screened.Optimal z-stack settings were applied in all collected images.Maximum Intensity Projections (MIPs) are presented throughout the manuscript.

Data Analysis-Neighboring Analysis
The distance between the relevant cell subsets (CD20 hi , PD1 hi CD57 hi/lo etc.) was calculated with Python 3.10.9using the SciPy library [25].The matrix interaction was created using X and Y coordinates from each cell phenotype, and the median distance was extracted.Furthermore, to characterize the probability of observing different patterns of cellular distribution across Regions Of Interest (ROIs) and patients, we studied the curves generated from the Ripley's G function and the theoretical Poisson curve using pointpats 2.3.0 (https://doi.org/10.5281/zenodo.7706219,accessed on 25 September 2023).The area between the empirical and theoretical Poisson curve was extracted using the NumPy library [26].ROIs with at least 20 positive cells for each cell subset under investigation were analyzed.The data were presented as (i) bar graphs, showing the range of all the various distances measured (X axis) and the frequency or count of B cells that fell within each distance range and (ii) dot plots, where each dot represents the mean value of the minimum distances between two cell populations for each follicular area.

Viral Load Measurement
The m2000 system (Abbott, Abbot Park, IL, USA) was used to perform an automated real-time polymerase chain reaction (PCR) for the determination of HIV plasma viral load (pVL), with a detection limit of 40 HIV RNA copies/mL.Flow cytometry with the AQUIOS Tetra-1 Panel in AQUIOS CL (Beckman Coulter Life Sciences, Indianapolis, IN, USA) was used to determine CD4 hi T cell counts.All PWLH involved in the current study were infected with clade B HIV.

Statistical Analysis
For the imaging data analysis, the Mann-Whitney test and simple linear regression analysis were used.The p-values of the Mann-Whitney test were corrected using the False Discovery Rate (FDR) correction test [27] with q = 0.05, for multiple comparisons (both uncorrected and corrected p values for each figure are shown in Supplementary Table S2).The analysis and graphs were generated using the GraphPad Prism 8.3.0 software.For statistical significance, a p value < 0.05 was considered.

Similar Profiles of Follicular Helper CD4 hi T Cell Densities in Viremic and cART HIV LNs
T FH cells are major contributors to HIV reservoir maintenance [28], as well as the development of broadly neutralizing antibodies [29]; we sought to investigate their in situ cell density in HIV-infected, compared to non-infected, tissues.We used tonsils and noninfected, cancer free, reactive LNs characterized by follicular hyperplasia as strict control groups (Figures S1 and S6) (Table 1).We started our analysis by employing a multiplex immunofluorescence imaging assay that allows for the simultaneous identification of major GC B and T cell subsets (Figure 1A).The gating strategy applied for the identification and quantitative analysis of relevant cell subsets by the Histo-cytometry pipeline [12,24] is shown (Figure 1B).The in situ density of CD20 hi/dim cells was used for the identification of individual follicular areas (highly enriched in CD20 hi/dim cells), as well as the 'total follicular' area (Figure 1B).As expected [5,30], PWLH tissues harbor both 'preserved' and 'irregular' follicular structures (Figures 1A and S1B).CD4 hi T cell subsets within the 'total follicular' area were analyzed based on their expression of PD1 and CD57 (Figure 1B).A preliminary analysis revealed low expression levels of CD4 in certain T FH cells, particularly the ones expressing CD57 (Figure S2A).Several studies have shown that T FH cells express a unique PD1 hi phenotype [9,10,31] compared to other CD4 hi T cells, while PD1 expression per cell (judged by Mean Fluorescence Intensity) of follicular CD8 hi T cells is 4-5 times lower than that of T FH [12].Therefore, the expression level of PD1 can serve as an in situ T FH identifier.To avoid inconsistencies and the misinterpretation of our data, especially for the CD57 hi T FH cell subset, we chose to directly analyze the PD1 hi CD57 hi/low cells in GCs (Figure 1B).The gating for setting the threshold of these biomarkers is shown in Figure S2A.Their expression level in extrafollicular areas, as well as the manual inspection of their fluorescence intensities in the raw images, was used as a reference to set the cut-off for 'high' values.The backgating of PD1 hi and CD57 hi cells identified by Histo-cytometry (shown as spheres) to the original image showed a high concordance between the digitally identified cells and their original counterparts identified by immunofluorescence staining (Figure 1C).cells in GCs (Figure 1B).The gating for setting the threshold of these biomarkers is shown in Figure S2A.Their expression level in extrafollicular areas, as well as the manual inspection of their fluorescence intensities in the raw images, was used as a reference to set the cut-off for high values.The backgating of PD1 hi and CD57 hi cells identified by Histo-cytometry (shown as spheres) to the original image showed a high concordance between the digitally identified cells and their original counterparts identified by immunofluorescence staining (Figure 1C). .Each symbol represents one donor.The p values were calculated using the Mann-Whitney test and were corrected using FDR correction with q = 0.05 (Supplementary Table S2).Dot graph (right) shows the distribution of PD1 hi cell densities among the samples.Each symbol represents a follicle.(E) Bar graph demonstrating the normalized per mm 2 numbers of follicular PD1 hi CD57 hi TFH cells in the same groups/subgroups of the samples.(F) Linear regression analysis to address the correlation between PD1 hi and PD1 hi CD57 hi absolute follicular counts between different subgroups.Each symbol represents a follicle.R-squared and p-values are displayed on graphs.(G) Linear regression analysis (lower) to address the correlation between normalized follicular PD1 hi counts and blood viral load-pVL.Dot graph (upper) showing the pVL differences (as a log scale) between the viremic HIV subgroups. .Each symbol represents one donor.The p values were calculated using the Mann-Whitney test and were corrected using FDR correction with q = 0.05 (Supplementary Table S2).Dot graph (right) shows the distribution of PD1 hi cell densities among the samples.Each symbol represents a follicle.(E) Bar graph demonstrating the normalized per mm 2 numbers of follicular PD1 hi CD57 hi T FH cells in the same groups/subgroups of the samples.(F) Linear regression analysis to address the correlation between PD1 hi and PD1 hi CD57 hi absolute follicular counts between different subgroups.Each symbol represents a follicle.R-squared and p-values are displayed on graphs.(G) Linear regression analysis (lower) to address the correlation between normalized follicular PD1 hi counts and blood viral load-pVL.Dot graph (upper) showing the pVL differences (as a log scale) between the viremic HIV subgroups.The calculation of PD1 hi cell densities (normalized cell counts per mm 2 ) allowed for the further grouping of HIV viremic and cART tissues into two subgroups characterized by significantly different cell densities of PD1 hi T FH cells: one with high PD1 hi T FH cell densities (hereafter 'high-T FH ') and one with low PD1 hi T FH cell densities (hereafter 'low-T FH ') (Figure 1D, left panel).To assess the heterogeneous (or not) prevalence of PD1 hi T FH cells across an individual tissue, the cell densities of PD1 hi T FH cells per follicle for every tissue were analyzed.A great variability in PD1 hi T FH cells was observed in all tissues, particularly in tonsils and control LNs (Figure 1D, right panel).
Next, the expression of CD57, a carbohydrate epitope that marks a T FH cell subset with a distinct positioning and function in human LNs [32,33] was investigated.As expected [33], the PD1 hi CD57 hi group represents a subset of T FH cells (Figure 1E).PD1 hi CD57 hi T FH cells exhibited a similar cell density profile to PD1 hi cells (Figure 1E) and a heterogenous prevalence across the tissue (Figure S2B).A strong correlation between PD1 hi and PD1 hi CD57 hi T FH cell densities was found for tonsil, control and cART LNs (Figure 1F).This association was less significant in the high-T FH PLWH viremic subgroup (Figure 1F).Although not statistically significant, a negative association was observed between the PD1 hi T FH cells and pVL in viremic PLWH, as well as a trend for a higher pVL in the low-T FH compared to high-T FH viremic PWLH subgroup (Figure 1G, lower and upper panel, respectively).Therefore, in agreement with our previous data for viremic SIV infection [10], two subgroups defined by significantly different cell densities of T FH cells were identified in viremic as well as cART HIV LNs.

A Distinct Positioning Profile of T FH Cells in HIV-Infected Compared to Non-Infected Tissues
Next, the spatial positioning of GC B cells and the T FH cell subsets was investigated in our tissue cohort.To this end, follicles from all groups, harboring at least 20 cells for each corresponding cell population, were used.The X, Y coordinates of the relevant cells were extracted (Figure 2A, upper panel), and a digitalized representation of their distribution was generated (Figure 2A, lower panel).Then, the 'G function' parameter, as a surrogate for the dispersion/scattering of a distribution, of a given cell type, as well as the mean values of the minimum distances between relevant cell types in individual follicular areas, was calculated [34].An example of follicles with a low and high mean distance between CD20 hi and PD1 hi cells and their associated G parameters is shown (Figure 2B).A similar distribution profile for CD20 hi cells among the tissue subgroups analyzed was detected (Figure 2C).Contrary to B cells, PD1 hi cells expressed a significantly higher dispersion in the vir-HIV subgroups compared to control LNs (Figure 2D).Regarding the PD1 hi cells distribution of cART tissues, a significant difference was found only between control and low-T FH cART LNs (Figure 2D).Furthermore, a significantly higher dispersion was found in low-T FH compared to high-T FH HIV tissues, in both viremic and cART tissues from PLWH (Figure 2D).A significantly higher mean of minimum distance between CD20 hi and PD1 hi cells was found in the vir-and cART HIV compared to control tissues (Figure 2E), while no difference was observed among the PLWH subgroups (Figure 2E).
Then, the aforementioned parameters were calculated for the PD1 hi CD57 lo and PD1 hi CD57 hi cells in all tissue groups.Given the low abundancy of CD57 hi T FH cells, a significantly lower number of follicular areas, especially in the low-T FH HIV subgroups, was analyzed (Figure 2F,G).Again, a comparable CD20 G-function profile was found among the tissue groups in these follicular areas (Figure S3A).Our data showed a similar G function and distance profile between tonsils and reactive control LNs (Figure 2F,G).In general, PD1 hi CD57 lo and PD1 hi CD57 hi cells express a significantly higher degree of dispersion in HIV-infected tissues compared to tonsils and control LNs (Figure 2F).Interestingly, the cART low-T FH subgroup was the one with the highest dispersion of PD1 hi CD57 lo cells among the PLWH subgroups (Figure 2F).With respect to the mean minimum distance, a trend, which was statistically significant for many of the comparisons, for a longer distance between CD20 hi and PD1 hi CD57 lo OR hi cells, was measured in HIV-infected tissues compared to tonsils and control LNs (Figure 2G).Our data suggest a distinct T FH cell in situ distribution profile, as well as a T FH -B cell proximity profile in PLWH, compared to tonsils and control LNs.The p values were calculated using the Mann-Whitney test and were corrected using FDR correction with q = 0.05 (Supplementary Table S2).

Significant Accumulation of Follicular Compared to Extrafollicular FOXP3 hi CD4 hi T Cells in cART Low-TFH LNs
We used the expression of FOXP3 as a surrogate for potential immune-regulatory CD4 hi T cells (Figure 3A).The Histo-cytometry gating scheme for the identification and calculation of FOXP3 hi CD4 hi T cells in extrafollicular and intrafollicular areas (highly enriched in CD20 hi/dim cells) is shown (Figure 3B).The concordance between digitally/Histocytometry-identified FOXP3 hi CD4 hi T cells (shown as spheres) and their original counterparts is demonstrated in Figure 3C.The lowest cell density of extrafollicular (EF) and follicular (F) FOXP3 hi CD4 hi T cells was found in tonsils and the highest in reactive control LNs (Figure 3D,E).Comparable EF-FOXP3 hi CD4 hi T cell densities among the PLWH .Each dot represents one follicle in all presented graphs.The p values were calculated using the Mann-Whitney test and were corrected using FDR correction with q = 0.05 (Supplementary Table S2).

Significant Accumulation of Follicular Compared to Extrafollicular FOXP3 hi CD4 hi T Cells in cART Low-T FH LNs
We used the expression of FOXP3 as a surrogate for potential immune-regulatory CD4 hi T cells (Figure 3A).The Histo-cytometry gating scheme for the identification and calculation of FOXP3 hi CD4 hi T cells in extrafollicular and intrafollicular areas (highly enriched in CD20 hi/dim cells) is shown (Figure 3B).The concordance between digitally/Histocytometry-identified FOXP3 hi CD4 hi T cells (shown as spheres) and their original counterparts is demonstrated in Figure 3C.The lowest cell density of extrafollicular (EF) and follicular (F) FOXP3 hi CD4 hi T cells was found in tonsils and the highest in reactive control LNs (Figure 3D,E).Comparable EF-FOXP3 hi CD4 hi T cell densities among the PLWH subgroups were found (Figure 3D).Although not statistically significant, a trend for higher cell densities of F-FOXP3 hi CD4 hi T cells in cART, compared to vir-HIV, LNs was measured (Figure 2E).A broad range of F-FOXP3 hi CD4 hi T cell densities was observed, particularly in the cART low-T FH subgroup (Figures 2E and S3B).
PLWH tissues tested (Figure 3F).However, the opposite profile was observed for the cART tissues (Figure 3F).A consistent, significantly higher cell density of F-FOXP3 hi CD4 hi compared to EF-FOXP3 hi CD4 hi T cells was measured, especially in the cART low-TFH subgroup (Figure 3F).The distribution profile (G function) of FOXP3 hi CD4 hi T cells across the whole imaged area for each tissue was also investigated.No significant differences were found among the groups analyzed (Figure S3C).Then, we focused our analysis on F-FOXP3 hi CD4 hi T cells.An example of the identification and corresponding digital representation of their distribution in cART tissues is shown (Figure 3G).A significantly lower dispersion was measured in the cART low-TFH compared to the cART high-TFH subgroup, when the G factor was calculated for F-FOXP3 hi CD4 hi T cells (Figure 3H).Our data show a preferential accumulation of follicular immune regulatory CD4 hi T cells in cART compared to viremic PLWH LNs. .The p values were calculated using the Mann-Whitney test and were corrected using FDR correction with q = 0.05 (Supplementary Table S2).
Next, the EF-and F-FOXP3 hi CD4 hi T cell densities were compared in each LN.Similar EF-and F-FOXP3 hi CD4 hi T cell densities were found in control LNs, while fewer F-FOXP3 hi CD4 hi compared to EF-FOXP3 hi CD4 hi T cells were found for almost all viremic PLWH tissues tested (Figure 3F).However, the opposite profile was observed for the cART tissues (Figure 3F).A consistent, significantly higher cell density of F-FOXP3 hi CD4 hi compared to EF-FOXP3 hi CD4 hi T cells was measured, especially in the cART low-T FH subgroup (Figure 3F).The distribution profile (G function) of FOXP3 hi CD4 hi T cells across the whole imaged area for each tissue was also investigated.No significant differences were found among the groups analyzed (Figure S3C).Then, we focused our analysis on F-FOXP3 hi CD4 hi T cells.An example of the identification and corresponding digital representation of their distribution in cART tissues is shown (Figure 3G).A significantly lower dispersion was measured in the cART low-T FH compared to the cART high-T FH subgroup, when the G factor was calculated for F-FOXP3 hi CD4 hi T cells (Figure 3H).Our data show a preferential accumulation of follicular immune regulatory CD4 hi T cells in cART compared to viremic PLWH LNs.

LN GrzB hi CD8 hi T Cells Are Negatively Associated with Blood Viral Load
The in situ profile of bulk and effector (GrzB hi )CD8 hi T cells (Figure 4A) was investigated using a multiplex imaging assay, and the Histo-cytometry gating scheme is shown in Figure 4B.The applied antibody panel (Supplementary Table S2, panel II) does not include a follicular/GC biomarker; therefore, the cell density of the CD8 hi T cell subsets was analyzed for the whole imaged area (Figure 4A,B).The concordance between digitally/Histo-cytometry-identified GrzB hi CD8 hi T cells (shown as spheres) and their original counterparts is demonstrated in Figure 4C.In line with our previous data [12,15], an accumulation of bulk and GrzB hi CD8 hi T cells was measured in viremic donors, particularly the high-T FH tissues, compared to tonsils and control LNs (Figures 4D and S4A).Although not significant, a reduction in GrzB hi CD8 hi T cells was observed between the viremic and cART tissues, which was more evident between the high-T FH subgroups (Figure 4D).A positive association between circulating and LN bulk as well as GrzB hi CD8 hi T cells was found in HIV viremic samples (Figure S4B).Contrary to circulating CD8 hi T cells (Figure S4C), a significant negative correlation was observed between the viral load and the LN GrzB hi CD8 hi T cells (Figure 4E). .The p values were calculated using the Mann-Whitney test and were corrected using FDR correction with q = 0.05 (Supplementary Table S2).

LN GrzB hi CD8 hi T Cells Are Negatively Associated with Blood Viral Load
The in situ profile of bulk and effector (GrzB hi )CD8 hi T cells (Figure 4A) was investigated using a multiplex imaging assay, and the Histo-cytometry gating scheme is shown in Figure 4B.The applied antibody panel (Supplementary Table S2, panel II) does not include a follicular/GC biomarker; therefore, the cell density of the CD8 hi T cell subsets was analyzed for the whole imaged area (Figure 4A,B).The concordance between digitally/Histo-cytometry-identified GrzB hi CD8 hi T cells (shown as spheres) and their original counterparts is demonstrated in Figure 4C.In line with our previous data [12,15], an accumulation of bulk and GrzB hi CD8 hi T cells was measured in viremic donors, particularly the high-TFH tissues, compared to tonsils and control LNs (Figures 4D and S4A).Although not significant, a reduction in GrzB hi CD8 hi T cells was observed between the viremic and cART tissues, which was more evident between the high-TFH subgroups (Figure 4D).A positive association between circulating and LN bulk as well as GrzB hi CD8 hi T cells was found in HIV viremic samples (Figure S4B).Contrary to circulating CD8 hi T cells (Figure S4C), a significant negative correlation was observed between the viral load and the LN GrzB hi CD8 hi T cells (Figure 4E).The p values were calculated using the Mann-Whitney test and corrected using FDR correction with q = 0.05 (Supplementary Table S2).
Next, the correlation between bulk and GrzB hi CD8 hi as well as FOXP3 hi CD4 hi T cell densities was investigated.A significant association was observed between the two CD8 hi T cell populations in viremic LNs (Figure S4D), as well as the cART low-T FH subgroup (Figure 4F).However, this was not the case for the cART high-T FH subgroup (Figure 4F).Among the groups analyzed, a positive correlation was found between bulk CD8 hi and EF-FOXP3 hi CD4 hi T cell densities in the cART low-T FH subgroup (Figure 4G, upper panel).This correlation was statistically significant between bulk CD8 hi T cells and F-FOXP3 hi CD4 hi T cells in the same subgroup (Figure 4G, lower panel).The distance profiling revealed a similar dispersion of GrzB hi CD8 hi T cells in LNs from cART, compared to those from viremic PLWH (Figure 4H).Conclusively, our data revealed an accumulation of GrzB hi CD8 hi T cells in PWLH LNs compared to non-infected tissues, which was inversely correlated with the viral load in viremic PLWH.

Differential Modulation of Innate Immune Cell Subsets by cART
HIV is a chronic disease characterized by immune activation and inflammation [5,35,36].Given the role of the innate immunity in HIV pathogenesis, we sought to investigate the tissue dynamics of several innate immune cell subsets using relevant biomarkers (CD163 and CD68, markers for monocytes/macrophages [37]; CD15, a surrogate for myeloid cells/granulocytes; CD16, a surrogate for activated myeloid cells, neutrophils and NK cells [38,39]) (Figure 5A).The Histo-cytometry gating scheme for the identification and quantification of these cell subsets is shown in Figure 5B.In general, a reduction was measured for all innate immune cell subsets analyzed in low-, compared to high-T FH viremic, subgroups (Figure 5C), that was significant for CD15 hi CD16 lo cells (Figure 5C).With respect to cART, lower cell densities of CD163 hi CD68 lo , CD15 hi CD16 lo , CD16 hi CD15 lo and CD16 hi CD15 hi cells were found in cART high-T FH compared to viremic high-T FH tissues (Figure 5C).Comparable cell densities of CD68 hi CD163 lo cells were counted among all tissue groups analyzed (Figure 5C, upper panel).
Then, the relationship between CD8 hi T cells and innate immunity cell types was investigated.A positive association between the CD68 hi or CD163 hi cell subsets and bulk CD8 hi T cells in viremic LNs was found (Figure S5A).A significant positive correlation between CD16 hi CD15 lo and bulk or Grzb hi CD8 hi T cells was found in the vir-HIV high-T FH group (Figure 5D).No such correlations were observed in cART LNs.Similar to Grzb hi CD8 hi T cells, a significant inverse correlation was also observed between CD16 hi CD15 lo cells and peripheral blood viral load in viremic PLWH (Figure S5B).An analysis of the in situ distribution profiling showed a higher dispersion in cART compared to viremic HIV-infected tissues for CD163 hi cells, while comparable profiles were found for CD68 hi and CD16 hi cells (Figure S5C).Furthermore, a lower minimum mean distance between GrzB hi CD8 hi and CD16 hi cells, but not CD68 hi or CD163 hi , in viremic compared to cART HIV tissues was found (Figures 5E and S5D).Altogether, our data suggest that the cell density of the individual innate cell types was differently affected in patients undergoing cART.S2).

Discussion
Here, we have investigated the immune cell landscape in reactive LNs from PLWH and compared it to non-infected control LNs and tonsils (Figure S6).We chose to compare the in situ immune dynamics in HIV LNs to non-infected, reactive LNs characterized by active follicles (follicular hyperplasia), as a reference for highly active F/GCs.Given the abundance in GC of B and TFH cells, as well as the preservation of the follicular and subfollicular structures, tonsils are considered as a prototype lymphoid organ for the investigation of the F/GC immune cell types.We should emphasize that none of the viremic PLWH had active opportunistic infections or AIDS-defining pathologies at the time of the sampling.The time since diagnosis was also similar among the individuals.Our analysis revealed two subgroups of HIV-infected LNs with respect to the cell density of TFH cells, in both viremic and cART PLWH.This profile, at least for viremic individuals, is in line with the TFH cell dynamics in the SIV non-human primate model [10,11].
No association of TFH cell densities with gender, age, CD4 hi and CD8 hi counts was found in either viremic or cART PLWH.HIV infection of LN CD4 hi T cells per se, intrinsic TFH cell factors and/or the interaction of TFH cells with the GC microenvironment represent  S2).

Discussion
Here, we have investigated the immune cell landscape in reactive LNs from PLWH and compared it to non-infected control LNs and tonsils (Figure S6).We chose to compare the in situ immune dynamics in HIV LNs to non-infected, reactive LNs characterized by active follicles (follicular hyperplasia), as a reference for highly active F/GCs.Given the abundance in GC of B and T FH cells, as well as the preservation of the follicular and sub-follicular structures, tonsils are considered as a 'prototype' lymphoid organ for the investigation of the F/GC immune cell types.We should emphasize that none of the viremic PLWH had active opportunistic infections or AIDS-defining pathologies at the time of the sampling.The time since diagnosis was also similar among the individuals.Our analysis revealed two subgroups of HIV-infected LNs with respect to the cell density of T FH cells, in both viremic and cART PLWH.This profile, at least for viremic individuals, is in line with the T FH cell dynamics in the SIV non-human primate model [10,11].
No association of T FH cell densities with gender, age, CD4 hi and CD8 hi counts was found in either viremic or cART PLWH.HIV infection of LN CD4 hi T cells per se, intrinsic T FH cell factors and/or the interaction of T FH cells with the GC microenvironment represent potential mechanisms that could contribute to the observed T FH cell in situ dynamics by altering the differentiation and/or turnover rate of total (or specific subset) T FH cells in PLWH.Regarding the viremic PLWH, we observed a negative association between pVL and GC/T FH cells that could reflect a preferential infection/loss of T FH cells or a generalized loss of T FH cells in highly viremic PLWH.The relatively low numbers (~5% of T FH cells) of HIV DNA+ T FH cells [9] suggest that the HIV infection of T FH cells per se may not be responsible for this negative association.Similar infection rates for CD57 hi and CD57 lo T FH cells have been reported previously [40] challenging the preferential infection/loss of CD57 hi T FH cells and the observed weaker association between these two T FH cell subsets in viremic compared to cART PLWH.Abortive HIV infection represents an alternative mechanism for the loss of CD4 T cells, at least in vitro [41].The role of such mechanisms in the regulation of vir-HIV T FH cells needs to be investigated.Alternatively, progressive fibrosis and damage of vital LN structural elements (e.g., the Fibroblastic Reticular Cell network [42]) and/or the loss of GC survival signals (e.g., due to damage of the Follicular Dendritic Cell network [43]) for T FH cells in high viremics could affect the differentiation and maintenance or turnover of T FH cells.The observed heterogeneity of T FH cell densities in different follicles across an individual tissue indicates that the locality of such mechanisms is possibly an important factor to consider in future studies.Our data urge for further investigation of LN structure elements, in conjunction with the in situ dynamics of immune cell types.The development of appropriate imaging tools will greatly facilitate such efforts.
Two groups (LoViReT and HiViReT) of treated PLWH, harboring a relatively wide range of very low viral reservoirs, was recently described [44].Whether the cART T FH subgroups correspond to a status is not known and merits further investigation.Despite viral control, cART is not able to fully restore the LN/follicular damage in PLWH back to normal.FRC reconstitution is one of the factors that could affect the reconstitution of the LN CD4 hi T cell pool and presumably T FH cell prevalence [45].No association of T FH cell densities with treatment duration was found.However, the capacity of individual PLWH to differentially respond to cART and restore relevant LN elements could contribute to the observed high-and low-T FH subgroups in the cART HIV group.Our distribution analysis showed an overall higher dispersion of PD1 hi T FH cells in HIV-infected compared to non-infected tissues, which is also associated with a longer mean distance between T FH and B cells in the infected donors.Previous studies have shown a reciprocal regulation between T FH and GC B cells [46].We hypothesize that the described spatial distribution profile may reflect a lower probability for these two cell subsets to interact in the infected LNs, leading to the subsequent loss of vital signals for T FH cells.The aforementioned profile was more evident in the low-T FH HIV subgroups, further supporting our hypothesis.
Follicular immune-regulatory CD4 hi T cells (T FR ) represent an important 'microenvironment cell factor' for the development of T FH cells [14,47,48].Tonsils harbor the lowest number of FOXP3 hi CD4 hi T cells, in line with previous reports [47,49].Our data revealed a contrasting profile regarding the cell density between EF-and F-FOXP3 hi CD4 hi T cells in control and viremic compared to cART-HIV LNs.Within the cART HIV group, we measured a significant increase in F-FOXP3 hi CD4 hi T, specifically in the cART-low T FH subgroup, suggesting a negative role for T FH cell development in these individuals.Supplementary to this is the significantly less scattered distribution of FOXP3 hi CD4 hi T cells within the follicles of low-compared to high-T FH cART tissues.Whether the FOXP3 hi CD4 hi T cells represent bona fide T FR cells or cells originating from T FH cells [50] is not known and needs further investigation.Our data urge for a comprehensive in situ phenotypic and functional characterization of FOXP3 hi CD4 hi T cells, especially in cART-HIV PLWH.
In contrast to PLWH, the majority of CD8 hi T cells in tonsils and control LNs express a GrzB lo phenotype, in line with our previous observations [12].The positive association between the numbers circulating and LN CD8 hi T cell density suggests that increased trafficking of bulk and presumably effector CD8 hi T cells may support, at least in part, their increased cell density in viremic LNs.A CXCR3/CXCR3L-mediated mechanism, which has been previously proposed for HIV PLWH [51] and SIV-infected non-human primates [15], could contribute to the CD8 hi T cell density profile we observed for viremic PLWH.Several studies have shown the role of CD8 hi T cells in controlling HIV and SIV [52,53].In line with these studies, we found a significant negative correlation between LN GrzB hi CD8 hi T cells and pVL that indicates a potential role of GrzB hi CD8 hi T cells in viral control, at least in part, in our cohort.An intermediate effector GrzB lo GrzK hi TOX hi TCF1 hi CD39 hi CD8 hi population negatively associated with plasma viremia was recently described in SIV/HIV-infected subjects [54].One could hypothesize that different CD8 hi T cell subsets can contribute to viral control.The relative impact of individual CD8 hi subsets in viremic and cART PLWH is not known and needs further investigation.Therefore, the comprehensive in situ phenotypic (e.g., expression of homing receptors), functional (e.g., 'regulatory' function [55]) and spatial characterization of CD8 hi T cell subsets, especially in cART-HIV PLWH, is of great interest, given their potential role for immunotherapies aiming to eliminate the virus.We found a positive correlation between CD8 hi and F-FOXP3 hi CD4 hi T cells, specifically in the low-T FH cART-HIV subgroup.Whether and how these two cell types could affect the cell density of T FH cells in this group remains to be elucidated.
Overall, we observed a diverse profile of innate immunity cell types among the groups.Individual cell types were differentially modulated by cART, while no consistent association with the low-or high-T FH status was observed.We measured different cell densities for the CD68 hi CD163 hi and CD68 lo CD163 hi cell subsets.CD163 is a receptor that can be cleaved, and therefore, the cell density of CD163 hi macrophages/monocytes can be underestimated.Our results, however, indicate that the possible cleavage/loss of CD163 is not responsible for the observed in situ dynamics [44].Our data point to a diverse innate immune LN microenvironment that could affect the host-virus interplay in these LNs [18].We found a strong correlation between LN CD8 hi T cells and CD16 hi CD15 lo cells in high-T FH viremic tissues.This profile was also associated with (i) a less dispersed CD8 hi T cell distribution, (ii) a shorter distance between CD8 hi and CD16 hi cells and (iii) a negative association between pVL and LN CD8 hi or CD16 hi CD15 lo cells.These findings urge for further investigation of neutrophils/granulocytes, in addition to macrophages, as possible key determinants for innate immunity/CD8 hi T cell crosstalk and virus dynamics [56].Whether the same or different innate immune cell subsets mediate the host-virus interaction in viremic and cART PLWH is not known and remains to be elucidated.
Altogether, we have analyzed several immunological cell types in a well-characterized cohort of LNs and provide evidence (i) for the subgrouping of HIV-infected LNs (both from viremic and cART PLWH) based on their T FH cell density, (ii) of a distinct profile of potential immunosuppressive FOXP3 hi CD4 hi T cells in cART LNs with respect to their cell densities, distribution between extrafollicular and follicular areas and spatial distribution within the follicular compartment, and (iii) the effect of cART on the cell density of CD8 hi T and innate immune cells (Table 2).Altered T FH cell density in HIV subgroups is associated with different CD8 hi and F-FOXP3 hi CD4 hi T cell density and distribution profiles, too.The data suggest that further investigation of CD8 hi and immune-regulatory CD4 hi T cells could provide insights for the T FH cell prevalence in HIV and particularly in cART PLWH.Understanding the follicular/GC microenvironment in HIV infection could further illuminate the role of T FH cells in HIV pathogenesis and possible combinatorial interventions aiming to manipulate a major HIV tissue reservoir.

Figure 1 .
Figure 1.Similar in situ cell density of TFH cells in viremic and cART PLWH LNs.(A) Representative examples of CD20 (red), CD4 (green), DAPI (blue), CD57 (gray) and PD1 (cyan) staining pattern from tonsil and control viremic and cART HIV LNs (scale bar: 30 µm). (B) The Histo-cytometry gating scheme used for the identification of TFH cell subsets based on their expression of PD1 and CD57 is shown.F and EF areas were manually identified based on the density of the CD20 signal, and the relevant cell counts were extracted for specific tissue localities.(C) Histo-cytometry-identified PD1 hi CD57 hi TFH cells were backgated to the original image using Imaris software.Each sphere represents one cell.(D) Bar graph (left) demonstrating the cell density (normalized per mm 2 counts) of follicular PD1 hi TFH cells in tonsils (N = 5), control LNs (N = 5), viremic HIV (N = 12) and cART HIV LNs (N = 20).Viremic and cART were further subdivided based on their TFH counts (HIV virhigh TFH (N = 6), HIV vir-low-TFH (N = 6), HIV cART-high-TFH (N = 6) and HIV cART-low-TFH (N = 14)).Each symbol represents one donor.The p values were calculated using the Mann-Whitney test and were corrected using FDR correction with q = 0.05 (Supplementary TableS2).Dot graph (right) shows the distribution of PD1 hi cell densities among the samples.Each symbol represents a follicle.(E) Bar graph demonstrating the normalized per mm 2 numbers of follicular PD1 hi CD57 hi TFH cells in the same groups/subgroups of the samples.(F) Linear regression analysis to address the correlation between PD1 hi and PD1 hi CD57 hi absolute follicular counts between different subgroups.Each symbol represents a follicle.R-squared and p-values are displayed on graphs.(G) Linear regression analysis (lower) to address the correlation between normalized follicular PD1 hi counts and blood viral load-pVL.Dot graph (upper) showing the pVL differences (as a log scale) between the viremic HIV subgroups.

Figure 1 .
Figure 1.Similar in situ cell density of T FH cells in viremic and cART PLWH LNs.(A) Representative examples of CD20 (red), CD4 (green), DAPI (blue), CD57 (gray) and PD1 (cyan) staining pattern from tonsil and control viremic and cART HIV LNs (scale bar: 30 µm). (B) The Histo-cytometry gating scheme used for the identification of T FH cell subsets based on their expression of PD1 and CD57 is shown.F and EF areas were manually identified based on the density of the CD20 signal, and the relevant cell counts were extracted for specific tissue localities.(C) Histo-cytometry-identified PD1 hi CD57 hi T FH cells were backgated to the original image using Imaris software.Each sphere represents one cell.(D) Bar graph (left) demonstrating the cell density (normalized per mm 2 counts) of follicular PD1 hi T FH cells in tonsils (N = 5), control LNs (N = 5), viremic HIV (N = 12) and cART HIV LNs (N = 20).Viremic and cART were further subdivided based on their T FH counts (HIV vir-high T FH (N = 6), HIV vir-low-T FH (N = 6), HIV cART-high-T FH (N = 6) and HIV cART-low-T FH (N = 14)).Each symbol represents one donor.The p values were calculated using the Mann-Whitney test and were corrected using FDR correction with q = 0.05 (Supplementary TableS2).Dot graph (right) shows the distribution of PD1 hi cell densities among the samples.Each symbol represents a follicle.(E) Bar graph demonstrating the normalized per mm 2 numbers of follicular PD1 hi CD57 hi T FH cells in the same groups/subgroups of the samples.(F) Linear regression analysis to address the correlation between PD1 hi and PD1 hi CD57 hi absolute follicular counts between different subgroups.Each symbol represents a follicle.R-squared and p-values are displayed on graphs.(G) Linear regression analysis (lower) to address the correlation between normalized follicular PD1 hi counts and blood viral load-pVL.Dot graph (upper) showing the pVL differences (as a log scale) between the viremic HIV subgroups.

Figure 2 .
Figure 2. A highly scattered distribution of TFH cells in HIV-infected compared to non-infected lymphoid tissues.(A) Representative immunofluorescence images showing the distribution of CD20 hi (blue) and PD1 hi (red) cells in follicles from one control, one vir-HIV high-TFH and one cART HIV high-TFH tissue (scale bar: 30 µm).The corresponding digitalized (generated by the Python distance analysis script) images are shown too.(B) The distribution bar graphs for the minimum distance between CD20 hi and PD1 hi cells in two follicular areas (control-left, cART-right) (upper panel).Diagrams showing the theoretical (blue) and experimental (red) Poisson curves for the distribution of PD1 hi cells in two follicular areas (control-left, cART-right) (lower panel).Bar graphs showing the G function analysis for total CD20 hi (C) and PD1 hi TFH cells (D) in individual follicles from control and infected LNs (control LNs (N = 54), vir-HIV high-TFH (N = 42), vir-HIV low-TFH (N = 51), cART HIV high-TFH (N = 31) and cART HIV low-TFH (N = 55)).(E) The mean of minimum distance values between CD20 hi and PD1 hi cells in individual follicles from control and infected LNs is shown (control LNs (N = 54), vir-HIV high-TFH (N = 42), vir-HIV low-TFH (N = 51), cART HIV high-TFH (N = 31) and cART HIV low-TFH (N = 55)).(F) The G function analysis for PD1 hi CD57 hi (left) and PD1 hi CD57 lo (right) TFH cells in tonsils, control and infected LNs is shown (tonsils (N = 29), control LNs (N = 29), vir-HIV high-TFH (N = 13), vir-HIV low-TFH (N = 5), cART HIV high-TFH (N = 11) and cART HIV low-TFH (N = 7)).(G) Bar graphs showing the mean of minimum distances between CD20 hi and PD1 hi CD57 lo (left) or PD1 hi CD57 hi (right) TFH cells in tonsils, control and infected LNs (tonsils (N = 29), control LNs (N = 29), vir-HIV high-TFH (N = 13), vir-HIV low-TFH (N = 5), cART HIV high-TFH (N = 11) and cART HIV low-TFH (N = 7)).Each dot represents one follicle in all presented graphs.The p values were calculated using the Mann-Whitney test and were corrected using FDR correction with q = 0.05 (Supplementary TableS2).

Figure 2 .
Figure 2. A highly scattered distribution of T FH cells in HIV-infected compared to non-infected lymphoid tissues.(A) Representative immunofluorescence images showing the distribution of CD20 hi (blue) and PD1 hi (red) cells in follicles from one control, one vir-HIV high-T FH and one cART HIV high-T FH tissue (scale bar: 30 µm).The corresponding digitalized (generated by the Python distance analysis script) images are shown too.(B) The distribution bar graphs for the minimum distance between CD20 hi and PD1 hi cells in two follicular areas (control-left, cART-right) (upper panel).Diagrams showing the theoretical (blue) and experimental (red) Poisson curves for the distribution of PD1 hi cells in two follicular areas (control-left, cART-right) (lower panel).Bar graphs showing the G function analysis for total CD20 hi (C) and PD1 hi T FH cells (D) in individual follicles from control and infected LNs (control LNs (N = 54), vir-HIV high-T FH (N = 42), vir-HIV low-T FH (N = 51), cART HIV high-T FH (N = 31) and cART HIV low-T FH (N = 55)).(E) The mean of minimum distance values between CD20 hi and PD1 hi cells in individual follicles from control and infected LNs is shown (control LNs (N = 54), vir-HIV high-T FH (N = 42), vir-HIV low-T FH (N = 51), cART HIV high-T FH (N = 31) and cART HIV low-T FH (N = 55)).(F) The G function analysis for PD1 hi CD57 hi (left) and PD1 hi CD57 lo (right) T FH cells in tonsils, control and infected LNs is shown (tonsils (N = 29), control LNs (N = 29), vir-HIV high-T FH (N = 13), vir-HIV low-T FH (N = 5), cART HIV high-T FH (N = 11) and cART HIV low-T FH (N = 7)).(G) Bar graphs showing the mean of minimum distances between CD20 hi and PD1 hi CD57 lo (left) or PD1 hi CD57 hi (right) T FH cells in tonsils, control and infected LNs (tonsils (N = 29), control LNs (N = 29), vir-HIV high-T FH (N = 13), vir-HIV low-T FH (N = 5), cART HIV high-T FH (N = 11) and cART HIV low-T FH (N = 7)).Each dot represents one follicle in all presented graphs.The p values were calculated using the Mann-Whitney test and were corrected using FDR correction with q = 0.05 (Supplementary TableS2).

Figure 3 .Figure 3 .
Figure 3. Preferential accumulation of FOXP3 hi CD4 hi T cells in cART HIV follicular areas.(A) Representative examples of CD20 (red), CD4 (green), DAPI (blue) and FOXP3 (yellow) staining pattern from tonsil and control viremic and cART HIV LNs (scale bar: 30 µm). (B) Histo-cytometry immunophenotyping gating strategy used for the sequential identification of FOXP3 hi CD4 hi T cells in follicular and extrafollicular areas in a control LN (C) Representative backgating of Histo-cytometryidentified FOXP3 hi CD4 hi T cells, using Imaris software.Each sphere represents one cell.(D) Bar graph (left) demonstrating the normalised per mm 2 counts of extrafollicular FOXP3 hi CD4 hi T cells in tonsils (N = 5), control LNs (N = 5), HIV vir-high-TFH (N = 6), HIV vir-low-TFH (N = 6), HIV cARThigh-TFH (N = 6) and HIV cART-low-TFH (N = 14).(E) Bar graph showing the normalised per mm 2 counts of follicular FOXP3 hi CD4 hi T cells in the same tissue samples.Each symbol represents one donor.(F) Bar graph with connecting lines demonstrating the normalized per mm 2 counts of FOXP3 hi CD4 hi T cells in follicular and extrafollicular regions in each tissue analyzed.(G) Figure 3. Preferential accumulation of FOXP3 hi CD4 hi T cells in cART HIV follicular areas.(A) Representative examples of CD20 (red), CD4 (green), DAPI (blue) and FOXP3 (yellow) staining pattern from tonsil and control viremic and cART HIV LNs (scale bar: 30 µm). (B) Histo-cytometry immunophenotyping gating strategy used for the sequential identification of FOXP3 hi CD4 hi T cells in follicular and extrafollicular areas in a control LN (C) Representative backgating of Histo-cytometryidentified FOXP3 hi CD4 hi T cells, using Imaris software.Each sphere represents one cell.(D) Bar graph (left) demonstrating the normalised per mm 2 counts of extrafollicular FOXP3 hi CD4 hi T cells in tonsils (N = 5), control LNs (N = 5), HIV vir-high-T FH (N = 6), HIV vir-low-T FH (N = 6), HIV cART-high-T FH (N = 6) and HIV cART-low-T FH (N = 14).(E) Bar graph showing the normalised per mm 2 counts of follicular FOXP3 hi CD4 hi T cells in the same tissue samples.Each symbol represents one donor.(F) Bar graph with connecting lines demonstrating the normalized per mm 2 counts of FOXP3 hi CD4 hi T cells in follicular and extrafollicular regions in each tissue analyzed.(G) Representative immunofluorescence images showing the distribution of FOXP3 hi (green) and CD20 hi (red) cells in follicles from one cART-high-T FH and one cART-low-T FH tissue.The corresponding digitalized (generated by the Python distance analysis script) images are shown too.(H) Bar graph showing the calculated G function values for follicular FOXP3 hi CD4 hi T cells in cART-high T FH (blue circles) and cART-low T FH (blue triangles) tissues (HIV cART-high T FH (N = 22) and HIV cART-low T FH (N = 64)).The p values were calculated using the Mann-Whitney test and were corrected using FDR correction with q = 0.05 (Supplementary TableS2).

Vaccines 2024 ,
12, 912 10 of 19 Representative immunofluorescence images showing the distribution of FOXP3 hi (green) and CD20 hi (red) cells in follicles from one cART-high-TFH and one cART-low-TFH tissue.The corresponding digitalized (generated by the Python distance analysis script) images are shown too.(H) Bar graph showing the calculated G function values for follicular FOXP3 hi CD4 hi T cells in cART-high TFH (blue circles) and cART-low TFH (blue triangles) tissues (HIV cART-high TFH (N = 22) and HIV cART-low TFH (N = 64))

Figure 4 .
Figure 4. Accumulated LN GrzB hi CD8 hi T cells are negatively associated with PLWH blood viral load.(A) Representative examples of GRZb (red), CD8 (green) and DAPI (blue) staining pattern from tonsil and control vir HIV and cART HIV LNs (scale bar: 30 µm). (B) Histo-cytometry gating strategy used for

Figure 5 .
Figure 5. Altered innate immunity signatures among control and HIV samples.(A) Representative examples of DAPI (blue), CD15 (cyan), CD16 (magenta), CD163 (yellow) and CD68 (red) staining pattern from tonsil and control vir-HIV and cART-HIV LNs (scale bar: 30 µm). (B) Histo-cytometry gating strategy used for the sequential identification of CD15 hi , CD16 hi , CD163 hi and CD68 hi innate cells.(C) Bar graphs demonstrating the normalized per mm 2 counts of bulk CD68 hi CD163 lo (upper left), CD68 lo CD163 hi (upper middle), CD68 hi CD163 hi (upper right), CD15 hi CD16 lo (lower left), CD15 hi CD16 lo (lower middle) and CD15 hi CD16 lo (lower right) in tonsils (N = 5), control LNs (N = 5), vir-HIV high-T FH (N = 6), vir-HIV low-T FH (N = 6), cART HIV high-T FH (N = 6) and cART HIV low-T FH (N = 14).Each symbol represents one donor.(D) Linear regression analysis to address the correlation between CD8 hi (upper panel) or GrzB hi CD8 hi (lower panel) T cells and CD16 hi CD15 lo cells in vir-HIV high-T FH tissues.(E) Bar graph showing the mean values of the minimum distances between GrzB hi CD8 hi T and CD16 hi cells in tissues from the HIV subgroups vir-HIV high-T FH (N = 6), vir-HIV low-T FH (N = 3), cART HIV high-T FH (N = 4) and cART HIV low-T FH (N = 10).Each dot represents a different donor.The p values were calculated using the Mann-Whitney test, and p values were corrected using FDR correction with q = 0.05 (Supplementary TableS2).
Figure S1.(A) Representative whole-tissue examples of CD20 (red), PD1 (green) and DAPI (blue) staining pattern from control tonsils, control LNs, vir HIV LNs and cART HIV LNs (scale bar: 300 µm).(B) Representative examples of CD20 (red), CD4 (green), DAPI (blue), CD57 (gray) and PD1 (cyan) staining pattern from a cART HIV LN (scale bar: 30 µm).The white line denotes the follicular area.Figure S2.(A) Immunophenotyping gating strategy used for the identification of TFH cells in lymphoid and tonsillar tissues of interest, based on the expression of PD1 and CD57, by Histo-cytometry.The extrafollicular expression level was used for setting the gates identifying the PD1 and CD57 subsets.An example from one LN and one tonsil is shown.(B) Dot plot graph showing the normalized numbers of PD1hiCD57hi TFH cells in all groups.Each symbol represents a follicular area.Figure S3.(A) Bar graph showing the G function values for CD20 hi B cells in all groups of tissues (control tonsils (N = 29), control LNs (N = 29), HIV vir-high T FH (N = 13), HIV vir-low T FH (N = 5), HIV cART-high T FH (N = 11) and HIV cART-low T FH (N = 7)).(B) Dot graph showing the distribution of normalized FOXP3 hi CD4 hi cell counts in tonsils, control LNs, vir HIV LNs and cART HIV LNs.Each symbol represents a follicle.(C) Bar graph showing the G function values for total FOXP3 hi CD4 hi T cells in control and HIV-infected LNs (control LNs (N = 5), HIV vir-high T FH (N = 6), HIV vir-low T FH (N = 6), HIV cART-high T FH (N = 6) and HIV cART-low T FH (N = 11)).Each symbol represents a donor.Figure S4.(A) Bar graphs demonstrating the frequency of bulk CD8+ (upper panel) and GrzB hi CD8+ (lower panel) cells in control tonsils (N = 5), control LNs (N = 5), HIV vir-high T FH (N = 6), HIV vir-low T FH (N = 6), HIV cART-high T FH (N = 6) and HIV cART-low T FH (N = 14).Each symbol represents a different donor.(B) Linear regression analysis between circulating CD8+ T cell frequencies and LN bulk or GrzB hi CD8+ T cells in viremic PLWH.(C) Linear regression analysis between blood viral loads and frequencies of circulating CD8+ T cells in viremic PLWH.(D) Linear regression analysis between LN bulk and GrzB hi CD8+ T cells in HIV viremic high-and low-T FH subgroups.Figure S5.(A) Linear regression analysis to show the correlation between LN CD68 hi CD163 hi (upper panel) or CD68 hi CD163 lo (lower panel) cells and LN CD8 hi cells in HIV vir LNs.(B) Linear regression analysis to show the correlation between LN CD16 hi CD15 lo and blood viral load in viremic PLWH.(C) Bar graphs showing the calculated G function values for CD163 hi (HIV vir-high T FH (N = 6), HIV vir-low T FH (N = 5), HIV cART-high T FH (N = 5) and HIV cART-low T FH (N = 10)), CD68 hi (HIV vir-high T FH (N = 6), HIV vir-low T FH (N = 5), HIV cART-high T FH (N = 5) and HIV cART-low T FH (N = 10)) and CD16 hi (HIV vir-high T FH (N = 6), HIV vir-low T FH (N = 3), HIV cART-high T FH (N = 4) and HIV cART-low T FH (N = 10)) LN cells in HIV subgroups.(D) Bar graphs showing the mean values of the minimum distances between GrzB hi CD8 hi T cells and CD68 hi (left panel) or CD163 hi (right panel) cells in the HIV subgroups (HIV vir-high T FH (N = 6), HIV vir-low T FH (N = 5), HIV cART-high T FH (N = 5) and HIV cART-low T FH (N = 10)).Each symbol represents a different donor.Figure S6.Hematoxylin and eosin (H&E) staining for all the LN tissues used in this study (scale bar: 1.5 mm).

Table 1 .
Demographic and clinical information of study PLWH.

Table 2 .
A table summarizing the main comparisons, with respect to cell densities, for immune cell subsets among the tissue groups.The relative prevalence of a given cell subset is denoted as VL= very low, L = low, H = high and VH = very high, among the groups.